An important use case of next-generation wireless systems is device-edge co-inference, where a semantic task is partitioned between a device and an edge server. The device carries out data collection and partial processing of the data, while the remote server completes the given task based on information received from the device. It is often required that processing and communication be run as efficiently as possible at the device, while more computing resources are available at the edge. To address such scenarios, we introduce a new system solution, termed neuromorphic wireless device-edge co-inference. According to it, the device runs sensing, processing, and communication units using neuromorphic hardware, while the server employs conventional radio and computing technologies. The proposed system is designed using a transmitter-centric information-theoretic criterion that targets a reduction of the communication overhead, while retaining the most relevant information for the end-to-end semantic task of interest. Numerical results on standard data sets validate the proposed architecture, and a preliminary testbed realization is reported.

翻译：下一代无线系统的一个重要应用场景是设备-边缘协同推断，其中语义任务在设备与边缘服务器之间进行划分。设备负责数据采集和部分数据处理，而远程服务器根据从设备接收的信息完成指定任务。通常要求设备端的处理与通信尽可能高效运行，而边缘端则拥有更丰富的计算资源。针对此类场景，我们提出一种新型系统方案——神经形态无线设备-边缘协同推断。该方案中，设备采用神经形态硬件运行感知、处理与通信单元，而服务器使用传统无线电与计算技术。所提系统基于以发射机为中心的信息理论准则进行设计，该准则旨在降低通信开销，同时保留与端到端语义任务最相关的信息。在标准数据集上的数值结果验证了所提架构的有效性，并报告了初步测试平台实现情况。